Sparse computations with Pei

1999 ◽  
pp. 1006-1014
Author(s):  
Frédérique Voisin ◽  
Guy-René Perrin
Keyword(s):  
Sparse Computations

scholarly journals Memory Optimizations For Fast Power-Aware Sparse Computations

2007 ◽  
Author(s):  
Konrad Malkowski ◽  
Padma Raghavan ◽  
Mary Jane Irwin
Keyword(s):  
Sparse Computations ◽  
Fast Power ◽  
Memory Optimizations

scholarly journals On the performance and energy efficiency of sparse linear algebra on GPUs

2016 ◽  
Vol 31 (5) ◽  
pp. 375-390 ◽  
Author(s):  
Hartwig Anzt ◽  
Stanimire Tomov ◽  
Jack Dongarra
Keyword(s):  
Energy Efficiency ◽  
Linear Algebra ◽  
Conjugate Gradient ◽  
Sparse Matrix ◽  
Preconditioned Conjugate Gradient ◽  
Matrix Product ◽  
Complex Simulation ◽  
Sparse Linear Algebra ◽  
Sparse Computations ◽  
Matrix Vector

In this paper we unveil some performance and energy efficiency frontiers for sparse computations on GPU-based supercomputers. We compare the resource efficiency of different sparse matrix–vector products (SpMV) taken from libraries such as cuSPARSE and MAGMA for GPU and Intel’s MKL for multicore CPUs, and develop a GPU sparse matrix–matrix product (SpMM) implementation that handles the simultaneous multiplication of a sparse matrix with a set of vectors in block-wise fashion. While a typical sparse computation such as the SpMV reaches only a fraction of the peak of current GPUs, we show that the SpMM succeeds in exceeding the memory-bound limitations of the SpMV. We integrate this kernel into a GPU-accelerated Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG) eigensolver. LOBPCG is chosen as a benchmark algorithm for this study as it combines an interesting mix of sparse and dense linear algebra operations that is typical for complex simulation applications, and allows for hardware-aware optimizations. In a detailed analysis we compare the performance and energy efficiency against a multi-threaded CPU counterpart. The reported performance and energy efficiency results are indicative of sparse computations on supercomputers.

A global address space framework for locality aware scheduling of block-sparse computations

2007 ◽  
Author(s):  
Sriram Krishnamoorthy ◽  
Umit Catalyurek ◽  
Jarek Nieplocha ◽  
Atanas Rountev ◽  
P. Sadayappan
Keyword(s):  
Address Space ◽  
Sparse Computations ◽  
Global Address Space

Compiler Transformation to Generate Hybrid Sparse Computations

2016 ◽  
Author(s):  
Huihui Zhang ◽  
Anand Venkat ◽  
Mary Hall
Keyword(s):  
Sparse Computations

scholarly journals HPF-2 Support for Dynamic Sparse Computations

1999 ◽  
pp. 230-246 ◽  
Author(s):  
R. Asenjo ◽  
O. Plata ◽  
E. L. Zapata ◽  
J. Touriño ◽  
R. Doallo
Keyword(s):  
Sparse Computations

15. Parallel Mapping of Sparse Computations

2011 ◽  
pp. 339-352
Author(s):  
Eric Robinson ◽  
Nadya Bliss ◽  
Sanjeev Mohindra
Keyword(s):  
Sparse Computations

Advanced Compiler Optimizations for Sparse Computations

1995 ◽  
Vol 31 (1) ◽  
pp. 14-24 ◽  
Author(s):  
A.J.C. Bik ◽  
H.A.G. Wijshoff
Keyword(s):  
Compiler Optimizations ◽  
Sparse Computations
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